SCIENCE@home

Automated flow cytometric analysis across large numbers of samples and cell types.

Xiaoyi Chen,Milena Hasan,V. Libri,A. Urrutia,Benoît Beitz,V. Rouilly,D. Duffy,E. Patin,B. Chalmond,L. Rogge,L. Quintana-Murci,M. Albert,Benno Schwikowski

Published 2015 in Clinical Immunology

ABSTRACT

Multi-parametric flow cytometry is a key technology for characterization of immune cell phenotypes. However, robust high-dimensional post-analytic strategies for automated data analysis in large numbers of donors are still lacking. Here, we report a computational pipeline, called FlowGM, which minimizes operator input, is insensitive to compensation settings, and can be adapted to different analytic panels. A Gaussian Mixture Model (GMM)-based approach was utilized for initial clustering, with the number of clusters determined using Bayesian Information Criterion. Meta-clustering in a reference donor permitted automated identification of 24 cell types across four panels. Cluster labels were integrated into FCS files, thus permitting comparisons to manual gating. Cell numbers and coefficient of variation (CV) were similar between FlowGM and conventional gating for lymphocyte populations, but notably FlowGM provided improved discrimination of "hard-to-gate" monocyte and dendritic cell (DC) subsets. FlowGM thus provides rapid high-dimensional analysis of cell phenotypes and is amenable to cohort studies.

PUBLICATION RECORD

CITATION MAP

EXTRACTION MAP

CLAIMS

  • No claims are published for this paper.

CONCEPTS

  • No concepts are published for this paper.

REFERENCES

Showing 1-8 of 8 references · Page 1 of 1

CITED BY

Showing 1-32 of 32 citing papers · Page 1 of 1